Topics in Applied Math
3.0
creditsAverage Course Rating
Analysis of Algorithms. This course in the probabilistic analysis of algorithms (AofA) will be accessible to any student who has had at least one course in probability and will be most beneficial to those who have had at least one probability course at the measure-theoretic level. The course will review basic topics from the theory of probability that have proved useful in AofA. It will provide introductions to more advanced AofA-relevant topics chosen from such topics as: Markov chains, branching processes, urn models, Poissonization (and de-Poissonization), various metrics on distributions, fixed-point characterizations of distributions, convergence of sequences of stochastic processes, perfect simulation using Markov chains (and otherwise), and large deviation principles. The course will interweave probability theory and applications to AofA, focusing on the fundamentally important and exceptionally rich example of limiting distributions for various ways of measuring the cost of executing the QuickSort and QuickSelect algorithms.
Spring 2014
Professor: James Fil
Many students agreed that after taking this course, their drawing abilities increased tremendously and they learned so much because each week there was a new topic. The professor was organized and his feedback was very helpful. The course was difficult to enroll into and is four hours long, but those seem to be the only cons about the course. Suggestions for improvement include: more class periods to decrease length of class and more variety of what students draw. Prospective students should be patient and be wil ing to have fun and learn.